השוואת שיטות

	גרדיאנט בוסטינג	רגרסיית הובר
תחום	למידת מכונה	סטטיסטיקה
משפחה	Machine learning	Regression model
שנת המקור	2001	1964
הוגה השיטה	Friedman, J. H.	Peter J. Huber
סוג	Ensemble (sequential boosting of decision trees)	Robust linear regression (M-estimation)
מקור מכונן	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.
כינויים	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu
קשורות	5	5
תקציר	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit.